Method of recommending data, electronic device, and medium

ABSTRACT

A method of recommending data, a device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of deep learning, natural language processing and intelligent recommendation technologies. The method of recommending the data includes: acquiring operation data of an operation object, and the operation data is associated with first content data and first target object data; determining an operation object feature, a content feature and a target object feature based on the operation data; determining a fusion feature based on the operation object feature and the content feature; and recommending second content data and second target object data in an associated manner based on the fusion feature and the target object feature.

This application claims priority of Chinese Patent Application No.202111428416.X filed on Nov. 26, 2021, which is incorporated herein inits entirety by reference.

TECHNICAL FIELD

The present disclosure relates to a field of an artificial intelligencetechnology, in particular to fields of deep learning, natural languageprocessing and intelligent recommendation technologies, and morespecifically, to a method of recommending data, an electronic device,and a medium.

BACKGROUND

In a related art, a content and a target object may generally berecommended in an associated manner, and the target object may include acommodity. However, a recommendation effect is poor due to an inaccurateassociation between the content and the target object.

SUMMARY

The present disclosure provides a method of recommending data, anelectronic device, and a storage medium.

According to an aspect of the present disclosure, a method ofrecommending data is provided, including: acquiring operation data of anoperation object, wherein the operation data is associated with firstcontent data and first target object data; determining an operationobject feature, a content feature and a target object feature based onthe operation data; determining a fusion feature based on the operationobject feature and the content feature; and recommending second contentdata and second target object data in an associated manner based on thefusion feature and the target object feature.

According to another aspect of the present disclosure, an electronicdevice is provided, including: at least one processor; and a memorycommunicatively connected to the at least one processor, wherein thememory stores instructions executable by the at least one processor, andthe instructions, when executed by the at least one processor, cause theat least one processor to implement the method of recommending the datadescribed above.

According to another aspect of the present disclosure, a non-transitorycomputer-readable storage medium having computer instructions therein isprovided, and the computer instructions are configured to cause acomputer to implement the method of recommending the data describedabove.

It should be understood that content described in this section is notintended to identify key or important features in embodiments of thepresent disclosure, nor is it intended to limit the scope of the presentdisclosure. Other features of the present disclosure will be easilyunderstood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used for better understanding of thesolution and do not constitute a limitation to the present disclosure,in which:

FIG. 1 schematically shows a system architecture of a method and anapparatus of recommending data according to embodiments of the presentdisclosure;

FIG. 2 schematically shows a flowchart of a method of recommending dataaccording to embodiments of the present disclosure;

FIG. 3 schematically shows a flowchart of a method of recommending dataaccording to other embodiments of the present disclosure;

FIG. 4 schematically shows a schematic diagram of association graph dataaccording to other embodiments of the present disclosure;

FIG. 5 schematically shows a flowchart of a method of recommending dataaccording to other embodiments of the present disclosure;

FIG. 6 schematically shows a schematic diagram of a method ofrecommending data according to embodiments of the present disclosure;

FIG. 7 schematically shows a block diagram of an apparatus ofrecommending data according to embodiments of the present disclosure;and

FIG. 8 shows a block diagram of an electronic device for performing adata recommendation for implementing embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Exemplary embodiments of the present disclosure will be described belowwith reference to accompanying drawings, which include various detailsof embodiments of the present disclosure to facilitate understanding andshould be considered as merely exemplary. Therefore, those of ordinaryskilled in the art should realize that various changes and modificationsmay be made to embodiments described herein without departing from thescope and spirit of the present disclosure. Likewise, for clarity andconciseness, descriptions of well-known functions and structures areomitted in the following descriptions.

Terms used herein are for the purpose of describing specific embodimentsonly and are not intended to limit the present disclosure. The terms“comprising”, “including”, “containing”, etc. used herein indicate thepresence of the feature, step, operation and/or component, but do notexclude the presence or addition of one or more other features, steps,operations or components.

All terms used herein (including technical and scientific terms) havethe meanings generally understood by those skilled in the art, unlessotherwise defined. It should be noted that the terms used herein shallbe interpreted to have meanings consistent with the context of thisspecification, and shall not be interpreted in an idealized or overlyrigid way.

In a case of using the expression similar to “at least one selected fromA, B, or C”, it should be explained according to the meaning of theexpression generally understood by those skilled in the art (forexample, “a system including at least one selected from A, B, or C”should include but not be limited to a system including only A, a systemincluding only B, a system including only C, a system including A and B,a system including A and C, a system including B and C, and/or a systemincluding A, B and C).

Embodiments of the present disclosure provide a method of recommendingdata, including: acquiring operation data of an operation object. Theoperation data is associated with first content data and first targetobject data. Then, an operation object feature, a content feature and atarget object feature are determined based on the operation data, and afusion feature is determined based on the operation object feature andthe content feature. Next, second content data and second target objectdata are recommended in an associated manner based on the fusion featureand the target object feature.

FIG. 1 schematically shows a system architecture of a method and anapparatus of recommending data according to embodiments of the presentdisclosure. It should be noted that FIG. 1 is merely an example of thesystem architecture to which embodiments of the present disclosure maybe applied to help those skilled in the art understand the technicalcontent of the present disclosure, but it does not mean that embodimentsof the present disclosure may not be applied to other devices, systems,environments or scenarios.

As shown in FIG. 1 , a system architecture 100 according to suchembodiments may include clients 101, 102 and 103, a network 104, and aserver 105. The network 104 is a medium for providing a communicationlink between the clients 101, 102, 103 and the server 105. The network104 may include various connection types, such as wired and/or wirelesscommunication links, optical fiber cables, or the like.

The clients 101, 102 and 103 may be used by a user to interact with theserver 105 through the network 104 to receive or send messages or thelike. The clients 101, 102 and 103 may be installed with variouscommunication client applications, such as shopping applications, webbrowser applications, search applications, instant messaging tools,email clients and/or social platform software, etc. (for example only).

The clients 101, 102 and 103 may be various electronic devices havingdisplay screens and supporting web browsing, including but not limitedto smart phones, tablet computers, laptop computers, desktop computers,or the like. The clients 101, 102 and 103 of embodiments of the presentdisclosure may run application programs, for example.

The server 105 may be a server providing various services, such as abackground management server (for example only) that provides a supportfor a website browsed by the user using the clients 101, 102 and 103.The background management server may analyze and process received datasuch as a user request, and feed back a processing result (such as a webpage, an information, or data acquired or generated according to theuser request) to the clients. In addition, the server 105 may be a cloudserver, that is, the server 105 may have a cloud computing function.

It should be noted that the method of recommending the data provided byembodiments of the present disclosure may generally be performed by theserver 105. Accordingly, the apparatus of recommending the data providedby embodiments of the present disclosure may be generally provided inthe server 105. The method of recommending the data provided byembodiments of the present disclosure may also be performed by a serveror server cluster different from the server 105 and capable ofcommunicating with the clients 101, 102, 103 and/or the server 105.Accordingly, the apparatus of recommending the data provided byembodiments of the present disclosure may also be provided in a serveror server cluster different from the server 105 and capable ofcommunicating with the clients 101, 102, 103 and/or the server 105.

In an example, the server 105 may acquire operation data from theclients 101, 102 and 103 through the network 104, and determine anoperation object feature, a content feature and a target object featurebased on the operation data, then determine a fusion feature based onthe operation object feature and the content feature, and finallyrecommend content data and target object data in an associated mannerbased on the fusion feature and the target object feature. For example,the content data and the target object data may be sent in an associatedmanner to the clients 101, 102 and 103.

It should be understood that the number of clients, network and servershown in FIG. 1 are merely schematic. According to implementation needs,any number of client, network and server may be provided.

Embodiments of the present disclosure provide a method of recommendingdata. The method of recommending the data according to exemplaryembodiments of the present disclosure will be described below withreference to FIG. 2 to FIG. 6 in combination with the systemarchitecture of FIG. 1 . The method of recommending the data ofembodiments of the present disclosure may be performed by, for example,the server shown in FIG. 1 , and the server shown in FIG. 1 is, forexample, the same as or similar to an electronic device below.

FIG. 2 schematically shows a flowchart of a method of recommending dataaccording to embodiments of the present disclosure.

As shown in FIG. 2 , a method 200 of recommending data of embodiments ofthe present disclosure may include, for example, operation S210 tooperation S240.

In operation S210, operation data of an operation object is acquired,and the operation data is associated with first content data and firsttarget object data.

In operation S220, an operation object feature, a content feature and atarget object feature are determined based on the operation data.

In operation S230, a fusion feature is determined based on the operationobject feature and the content feature.

In operation S240, second content data and second target object data arerecommended in an associated manner based on the fusion feature and thetarget object feature.

Exemplarily, the first content data or the second content data mayinclude, for example, an article, news, or other data. The first targetobject data or the second target object data may include, for example, acommodity, a product, or other data. Here, unless otherwise specified,content data may refer to the first content data, the second contentdata, third content data (described below) or historical content data(described below), and target object data may refer to the first targetobject data, the second target object data, third target object data(described below) or historical target object data (described below). Ingeneral, the content data and the target object data may be recommendedin an associated manner. For example, when the content data isrecommended, the target object data associated with the content data maybe recommended together. Since the content data and the target objectdata are associated, the target object data may generally be concernedby the operation object when the operation object browses the contentdata.

The operation data of the operation object may be, for example, for thefirst content data or the first target object data recommended in anassociated manner. The operation data may include, for example, clickingdata, browsing data, data generated by performing a resource transfer onthe first target object data, and so on.

Since the operation data indicates an intrinsic relationship between theoperation object, the first content data and the first target objectdata, it is possible to determine the operation object feature, thecontent feature and the target object feature based on the operationdata. The operation object feature, the content feature and the targetobject feature may be, for example, associated with each other.

Next, the operation object feature and the content feature may be fusedto obtain a fusion feature, and the second content data and the secondtarget object data may be recommended in an associated manner based on asimilarity between the fusion feature and the target object feature. Therecommended second content data corresponds to the content feature, andthe recommended second target object data corresponds to the targetobject feature.

According to embodiments of the present disclosure, the operation objectfeature, the content feature and the target object feature aredetermined based on the operation data, then the fusion feature isdetermined based on the operation object feature and the contentfeature, and the second content data and the second target object dataare recommended in an associated manner based on the similarity betweenthe fusion feature and the target object feature. It may be understoodthat richer fusion feature and target object feature associated witheach other may be obtained based on the operation data, so that arichness and a recommendation effect of the recommended data may beimproved by recommending the second content data and the second targetobject data based on the fusion feature and the target object featureassociated with each other. In addition, when the recommended secondcontent data is browsed by the operation object, the second targetobject data associated with the second content data may also beconcerned by the operation object, so that a degree of concern on thetarget object is improved.

FIG. 3 schematically shows a flowchart of a method of recommending dataaccording to other embodiments of the present disclosure.

As shown in FIG. 3 , a method 300 of recommending data of embodiments ofthe present disclosure may include, for example, operation S301 tooperation S308.

In operation S301, at least one content label and at least one targetobject label are acquired.

Exemplarily, for at least one historical content data stored in acontent library, a data processing may be performed on the at least onehistorical content data by using at least one selected from a firstnatural language processing model or a second deep learning model, so asto obtain at least one content label respectively corresponding to theat least one historical content data.

For example, taking the historical content data including articles as anexample, a label extraction may be performed on a title, a body and anintroduction information of each article by using the first naturallanguage processing model, so as to obtain structured data. Thestructured data may include, for example, an article title, an articlepublication time, an author name, an article category, and so on. Inaddition, it is also possible to process a content of the article, animage in the article, comment data for the article, and so on by usingthe second deep learning model, so as to obtain description data for thearticle. The structured data and the description data may be used as thecontent label corresponding to the article.

Exemplarily, for at least one historical target object data stored in atarget object library, a data processing may be performed on the atleast one historical target object data by using at least one selectedfrom a second natural language processing model or a third deep learningmodel, so as to obtain at least one target object label respectivelycorresponding to the at least one historical target object data.

For example, taking the historical target object data includingcommodity data as an example, a label extraction may be performed on aname, a creative information and a category information of eachcommodity data by using the second natural language processing model, soas to obtain structured data. The structured data may include, forexample, a color, a price, a brand, etc. of a commodity. In addition, itis also possible to process an image of the commodity, comment data forthe commodity, and so on by using the third depth learning model, so asto obtain description data for the commodity. The structured data andthe description data may be used as the target object labelcorresponding to the commodity data.

In operation S302, a content label and a target object label associatedwith each other are determined based on a first similarity between theat least one content label and the at least one target object label, andthe first similarity between the content label and the target objectlabel associated with each other meets a first similarity condition.

For example, each content label and each target object label may beconverted into vectors, and then a distance between the vector of eachcontent label and the vector of each target object label may becalculated by means of semantic matching. The distance between thevectors may represent the first similarity between the content label andthe target object label. Then, the content label and the target objectlabel with a large similarity may be determined based on the distancebetween the vectors, and the content label and the target object labelwith the large similarity may be used as the content label and thetarget object label associated with each other.

In operation S303, third content data and third target object data arerecommended in an associated manner based on the content label and thetarget object label associated with each other.

Exemplarily, the third content data is, for example, at least part ofthe historical content data, and the third target object data is, forexample, at least part of the historical target object data.

For example, the content label and the target object label associatedwith each other indicate a high probability of an association betweenthe corresponding third content data and the corresponding third targetobject data. Therefore, it is highly possible that the third targetobject data may be of interest to the operation object interested in thethird content data. Then, the third content data and the third targetobject data may be recommended in an associated manner based on thecontent label and the target object label associated with each other.For example, when the third content data is recommended, the associatedthird target object data may also be recommended, so that a degree ofconcern on the third target object data by the operation object browsingthe third content data may be improved.

In operation S304, operation data of the operation object is acquired.

In operation S305, first content data and first target object dataassociated with each other corresponding to the operation data aredetermined from the third content data and the third target object datarecommended in the associated manner.

Exemplarily, the first content data is, for example, at least part ofthe third content data, and the first target object data is, forexample, at least part of the third target object data. The operationdata may represent, for example, operations such as clicking, browsing,and transferring a virtual resource performed by the operation object onthe first content data or the first target object data. Therefore, thefirst content data and the first target object data associated with eachother corresponding to the operation data may be determined from thethird content data and the third target object data recommended in theassociated manner.

Since the first content data and the first target object data associatedwith each other are determined based on the operation data, it is highlypossible that the first target object data is of interest to theoperation object interested in the first content data. Therefore, theassociation between the determined first content data and the determinedfirst target object data is more accurate.

In operation S306, a content label associated with the operation objectand a target object label associated with the operation object aredetermined based on the first content data and the first target objectdata associated with each other corresponding to the operation data.

For example, the first content label and the first target object labelassociated with each other may be represented by a “content label-targetobject label pair”. After a plurality of “content label-target objectlabel pairs” with a large similarity are determined in theabove-mentioned operation S302, the third content data and the thirdtarget object data may be recommended in an associated manner based onthe plurality of “content label-target object label pairs” with thelarge similarity. A part of the third content data and the third targetobject data (that is, the first content data and the first target objectdata) recommended in the associated manner may be operated by theoperation object to generate the operation data. A part of the “contentlabel-target object label pairs” concerned by the operation object maybe determined based on the operation data.

In operation S307, an operation object label is determined based on thecontent label associated with the operation object and the target objectlabel associated with the operation object.

In operation S308, association graph data is determined based on theoperation object label, the content label associated with the operationobject, and the target object label associated with the operationobject.

For the part of “content label-target object label pairs” concerned bythe operation object, the content label and the target object labelconcerned by the operation object may be used as the operation objectlabel. Then, the association graph data may be determined based on theoperation object label, the content label concerned by the operationobject, and the target object label concerned by the operation object.

FIG. 4 schematically shows a schematic diagram of association graph dataaccording to other embodiments of the present disclosure.

As shown in FIG. 4 , a bipartite graph of “operation objectlabel-content label” and “operation object label-target object label” isconstructed based on the operation data of the operation object by usinga knowledge graph technology. The bipartite graph is an associationgraph.

For example, if the third content data and the third target object datacorresponding to a “content label 2-target object label 1 pair” arerecommended in an associated manner, and an operation is performed by anoperation object corresponding to an operation object label 1 on thethird content data and the third target object data recommended in theassociated manner, it indicates that the third content data and thethird target object data recommended are concerned by the operationobject corresponding to the operation object label 1, then the thirdcontent data and the third target object data associated with each otherconcerned by the operation object may be determined as the first contentdata and the first target object data associated with each other.Therefore, the content label 2 and the target object label 1 may be usedas the operation object label 1. In this way, a plurality of operationobject labels may be obtained. Then, a bipartite graph (an associationgraph) is constructed based on the operation object labels, the contentlabels, and the target object labels. As shown in the bipartite graph,the operation object label 1 is associated with the content label 2 andthe target object label 1.

According to embodiments of the present disclosure, feature data isobtained through the association graph, and an accuracy of the featuredata may be improved, so that an effect of a data recommendation basedon the feature data may be improved.

FIG. 5 schematically shows a flowchart of a method of recommending dataaccording to other embodiments of the present disclosure.

As shown in FIG. 5 , on the basis of embodiments disclosed in FIG. 3 , amethod 500 of recommending data of embodiments of the present disclosuremay further include, for example, operation S509 to operation S513.Operation S509 to operation S513 may be performed, for example, afteroperation S308 shown in FIG. 3 is performed.

In operation S509, the association graph data is input into a first deeplearning model to obtain an operation object feature, a content feature,and a target object feature.

Since the content label, the target object label and the operationobject label are obtained based on semantics, it may be difficult forthe labels, to some extent, to express an intrinsic relationship betweenthe content, the target object and the operation object. In view ofthis, it is possible to input the association graph data into a trainedfirst depth learning model for recognition. The intrinsic relationshipbetween the association graph data may be recognized by the first depthlearning model, so as to obtain the operation object feature, thecontent feature and the target object feature. The operation objectfeature, the content feature and the target object feature may be, forexample, feature vectors. The first deep learning model may include, forexample, a graph neural network.

The operation object feature may indicate, for example, the intrinsicrelationship between the operation object and the content and theintrinsic relationship between the operation object and the targetobject. The content feature may indicate, for example, the intrinsicrelationship between the content and the operation object and theintrinsic relationship between the content and the target object. Thetarget object feature may indicate, for example, the intrinsicrelationship between the target object and the content and the intrinsicrelationship between the target object and the operation object.

In operation S510, a fusion feature is determined based on the operationobject feature and the content feature.

In operation S511, a second similarity between the fusion feature andthe target object feature is determined.

For example, the fusion feature may be obtained by fusing the operationobject feature and the content feature. In an example, the vectorcorresponding to the operation object feature and the vectorcorresponding to the content feature may be added to obtain the fusionfeature.

In a case that the second similarity meets a second similaritycondition, for a content feature associated with the fusion feature,second content data corresponding to the content feature and secondtarget object data are recommended in an associated manner.

For example, the second similarity may be represented by a distancebetween vectors. A large similarity between the fusion feature and thetarget object feature indicates a large degree of association betweenthe second content data corresponding to the content feature and thesecond target object data corresponding to the target object feature,and the second target object data may be of interest to the operationobject interested in the second content data. Therefore, the secondcontent data and the second target object data of interest to theoperation object may be recommended in an associated manner.

In embodiments of the present disclosure, a small number of “contentlabel-target object label pairs” may be obtained based on semantics, andit may be difficult to deeply reflect the intrinsic association betweenthe content data and the target object data. Therefore, it is possibleto determine a large number of fusion features and target objectfeatures with a large similarity through the association graph data, andrecommend the second content data and the second target object dataassociated with each other based on the large number of fusion featuresand target object features associated with each other, so that arecommendation effect and a degree of concern on the second targetobject data may be improved.

In another example, a data recommendation may be performed for theoperation object, which may specifically include operation S512 tooperation S513.

In operation S512, a candidate operation object corresponding to anoperation object feature associated with the fusion feature isdetermined.

In operation S513, the second content data corresponding to the contentfeature and the second target object data are recommended in anassociated manner to the candidate operation object.

Since the fusion feature is obtained based on the content feature andthe operation object feature, for the operation object featurecorresponding to the fusion feature, the operation object correspondingto the operation object feature may be determined as the candidateoperation object.

Then, when the second content data and the second target object data arerecommended in an associated manner, the second content data and thesecond target object data may be recommended to the candidate operationobject.

According to embodiments of the present disclosure, since the fusionfeature and the operation object feature are associated, when the secondcontent data and the second target object data associated with eachother are recommended based on the similarity between the fusion featureand the target object feature, the data may be recommended to thecandidate operation object, so that the data recommendation is moretargeted.

FIG. 6 schematically shows a schematic diagram of a method ofrecommending data according to embodiments of the present disclosure.

As shown in FIG. 6 , embodiments of the present disclosure include, forexample, operation S601 to operation S612.

In operation S601, for historical target object data stored in a targetobject library, the historical target object data is processed by atarget object label extraction module to obtain a target object label.The target object label extraction module may include, for example, anatural language processing model and a deep learning model.

In operation S602, the extracted target object label is stored in atarget object label library.

In operation S603, for historical content data stored in a contentlibrary, the historical content data is processed by a content labelextraction module to obtain a content label. The content labelextraction module may include, for example, a natural languageprocessing model and a deep learning model.

In operation S604, the extracted content label is stored in a contentlabel library.

In operation S605, the target object label and the content label areprocessed based on a semantic matching module, so as to obtain anassociated “content label-target object label pair”.

The semantic matching module may include, for example, a trainedsemantic matching model, which may have two functions, for example. Onefunction is to vectorize the target object label and the content label,and the other is to perform a similarity matching between a vectorcorresponding to the target object label and a vector corresponding tothe content label, and determine the content label and the target objectlabel with a large similarity as the “content label-target object labelpair”. In this way, the “content label-target object label pair” isassociated based on semantics.

In operation S606, the associated “content label-target object labelpair” is stored into an association library.

In operation S607, third content data and third target object data arerecommended in an associated manner based on the “content label-targetobject label pair” in the association library.

In operation S608, an operation performed by the operation object onfirst content data and first target object data recommended in anassociated manner is received.

For example, the first content data is at least part of the thirdcontent data, and the first target object data is at least part of thethird target object data.

In operation S609, operation data is obtained based on the operationperformed by the operation object on the recommended data.

In operation S610, the operation data is sent to an interest matchingmodule.

In operation S611, a fusion feature and a target object featureassociated with each other are obtained by the interest matching modulebased on the operation data, the target object label and the contentlabel.

For example, for the first content data and the first target object dataassociated with each other corresponding to the operation data, theoperation object label may be determined based on the content featureand the target object label. Then, association graph data is determinedbased on the operation object label, the content label associated withthe operation object label, and the target object label associated withthe operation object. Then, the operation object feature, the contentfeature and the target object feature are obtained based on theassociation graph data, and the fusion feature is determined based onthe operation object feature and the content feature. The operationobject feature indicates an interest information of the operation objectfor the content and the target object.

In operation S612, the fusion feature and the target object featureassociated with each other are stored in the association library, sothat the second content data and the second target object data arerecommended in an associated manner based on the fusion feature and thetarget object feature associated with each other.

Exemplarily, the fusion feature and the target object feature associatedwith each other are stored in the association library, so that a contentof the association library is enriched, and an effect of the datarecommendation is improved. For example, a candidate operation objectcorresponding to the operation object feature associated with the fusionfeature is determined based on the similarity between the fusion featureand the target object feature, and the content data and the targetobject data are recommended to the candidate operation object in anassociated manner, so that the data recommendation may be more targeted.

FIG. 7 schematically shows a block diagram of an apparatus ofrecommending data according to embodiments of the present disclosure.

As shown in FIG. 7 , an apparatus 700 of recommending data ofembodiments of the present disclosure includes, for example, a firstacquisition module 710, a first determination module 720, a seconddetermination module 730, and a first recommendation module 740.

The first acquisition module 710 may be used to acquire operation dataof an operation object, and the operation data is associated with firstcontent data and first target object data. According to embodiments ofthe present disclosure, the first acquisition module 710 may perform,for example, the operation S210 described above with reference to FIG. 2, which will not be described in detail here.

The first determination module 720 may be used to determine an operationobject feature, a content feature and a target object feature based onthe operation data. According to embodiments of the present disclosure,the first determination module 720 may perform, for example, theoperation S220 described above with reference to FIG. 2 , which will notbe described in detail here.

The second determination module 730 may be used to determine a fusionfeature based on the operation object feature and the content feature.According to embodiments of the present disclosure, the seconddetermination module 730 may perform, for example, the operation S230described above with reference to FIG. 2 , which will not be describedin detail here.

The first recommendation module 740 may be used to recommend secondcontent data and second target object data in an associated manner basedon the fusion feature and the target object feature. According toembodiments of the present disclosure, the first recommendation module740 may perform, for example, the operation S240 described above withreference to FIG. 2 , which will not be described in detail here.

According to embodiments of the present disclosure, the apparatus 700may further include a second acquisition module used to acquire at leastone content label and at least one target object label. The firstdetermination module 720 may include a first determination sub-module, asecond determination sub-module, a third determination sub-module, andan input sub-module. The first determination sub-module may be used todetermine, based on the operation data, a content label associated withthe operation object and a target object label associated with theoperation object from the at least one content label and the at leastone target object label. The second determination sub-module may be usedto determine an operation object label based on the content labelassociated with the operation object and the target object labelassociated with the operation object. The third determination sub-modulemay be used to determine association graph data based on the operationobject label, the content label associated with the operation object,and the target object label associated with the operation object. Theinput sub-module may be used to input the association graph data into afirst deep learning model to obtain the operation object feature, thecontent feature and the target object feature.

According to embodiments of the present disclosure, the apparatus 700may further include a third determination module and a secondrecommendation module. The third determination module may be used todetermine a content label and a target object label associated with eachother, based on a first similarity between the at least one contentlabel and the at least one target object label, and the first similaritybetween the content label and the target object label associated witheach other meets a first similarity condition. The second recommendationmodule may be used to recommend third content data and third targetobject data in an associated manner based on the content label and thetarget object label associated with each other. The first content datais at least part of the third content data, and the first target objectdata is at least part of the third target object data.

According to embodiments of the present disclosure, the firstdetermination sub-module includes a first determination unit and asecond determination unit. The first determination unit may be used todetermine first content data and first target object data correspondingto the operation data from the third content data and the third targetobject data recommended in the associated manner, and the first contentdata and the first target object data are associated with each other.The second determination unit may be used to determine, based on thefirst content data and the first target object data associated with eachother corresponding to the operation data, the content label associatedwith the operation object and the target object label associated withthe operation object.

According to embodiments of the present disclosure, the firstrecommendation module 740 includes a fourth determination sub-module anda recommendation sub-module. The fourth determination sub-module may beused to determine a second similarity between the fusion feature and thetarget object feature. The recommendation sub-module may be used torecommend, for a content feature associated with the fusion feature, thesecond content data corresponding to the content feature and the secondtarget object data in the associated manner, in response to the secondsimilarity meeting a second similarity condition.

According to embodiments of the present disclosure, the recommendationsub-module includes a third determination unit and a recommendationunit. The third determination unit may be used to determine a candidateoperation object corresponding to an operation object feature associatedwith the fusion feature. The recommendation unit may be used torecommend the second content data corresponding to the content featureand the second target object data to the candidate operation object inthe associated manner.

According to embodiments of the present disclosure, the secondacquisition module includes a first processing sub-module and a secondprocessing sub-module. The first processing sub-module may be used toperform data processing on at least one historical content data by usingat least one selected from a first natural language processing model ora second deep learning model, so as to obtain at least one content labelcorresponding to each historical content data. The second processingsub-module may be used to perform data processing on at least onehistorical target object data by using at least one selected from asecond natural language processing model and a third deep learningmodel, so as to obtain at least one target object label corresponding toeach historical target object data.

In the technical solution of the present disclosure, an acquisition, astorage, a use, a processing, a transmission, a provision, a disclosure,and an application of user personal information involved comply withprovisions of relevant laws and regulations, take essentialconfidentiality measures, and do not violate public order and goodcustom. In the technical solution of the present disclosure,authorization or consent is obtained from the user before the user'spersonal information is obtained or collected.

According to embodiments of the present disclosure, the presentdisclosure further provides an electronic device, a readable storagemedium, and a computer program product.

FIG. 8 shows a block diagram of an electronic device for performing adata recommendation for implementing embodiments of the presentdisclosure.

FIG. 8 shows a schematic block diagram of an exemplary electronic device800 for implementing embodiments of the present disclosure. Theelectronic device 800 is intended to represent various forms of digitalcomputers, such as a laptop computer, a desktop computer, a workstation,a personal digital assistant, a server, a blade server, a mainframecomputer, and other suitable computers. The electronic device mayfurther represent various forms of mobile devices, such as a personaldigital assistant, a cellular phone, a smart phone, a wearable device,and other similar computing devices. The components as illustratedherein, and connections, relationships, and functions thereof are merelyexamples, and are not intended to limit the implementation of thepresent disclosure described and/or required herein.

As shown in FIG. 8 , the electronic device 800 includes a computing unit801 which may perform various appropriate actions and processesaccording to a computer program stored in a read only memory (ROM) 802or a computer program loaded from a storage unit 808 into a randomaccess memory (RAM) 803. In the RAM 803, various programs and datanecessary for an operation of the electronic device 800 may also bestored. The computing unit 801, the ROM 802 and the RAM 803 areconnected to each other through a bus 804. An input/output (I/O)interface 805 is also connected to the bus 804.

A plurality of components in the electronic device 800 are connected tothe I/O interface 805, including: an input unit 806, such as a keyboard,or a mouse; an output unit 807, such as displays or speakers of varioustypes; a storage unit 808, such as a disk, or an optical disc; and acommunication unit 809, such as a network card, a modem, or a wirelesscommunication transceiver. The communication unit 809 allows theelectronic device 800 to exchange information/data with other devicesthrough a computer network such as Internet and/or varioustelecommunication networks.

The computing unit 801 may be various general-purpose and/or dedicatedprocessing assemblies having processing and computing capabilities. Someexamples of the computing units 801 include, but are not limited to, acentral processing unit (CPU), a graphics processing unit (GPU), variousdedicated artificial intelligence (AI) computing chips, variouscomputing units that run machine learning model algorithms, a digitalsignal processing processor (DSP), and any suitable processor,controller, microcontroller, etc. The computing unit 801 executesvarious methods and steps described above, such as the method ofrecommending the data. For example, in some embodiments, the method ofrecommending the data may be implemented as a computer software programwhich is tangibly embodied in a machine-readable medium, such as thestorage unit 808. In some embodiments, the computer program may bepartially or entirely loaded and/or installed in the electronic device800 via the ROM 802 and/or the communication unit 809. The computerprogram, when loaded in the RAM 803 and executed by the computing unit801, may execute one or more steps in the method of recommending thedata described above. Alternatively, in other embodiments, the computingunit 801 may be configured to perform the method of recommending thedata by any other suitable means (e.g., by means of firmware).

Various embodiments of the systems and technologies described herein maybe implemented in a digital electronic circuit system, an integratedcircuit system, a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), an application specific standardproduct (ASSP), a system on chip (SOC), a complex programmable logicdevice (CPLD), a computer hardware, firmware, software, and/orcombinations thereof. These various embodiments may be implemented byone or more computer programs executable and/or interpretable on aprogrammable system including at least one programmable processor. Theprogrammable processor may be a dedicated or general-purposeprogrammable processor, which may receive data and instructions from astorage system, at least one input device and at least one outputdevice, and may transmit the data and instructions to the storagesystem, the at least one input device, and the at least one outputdevice.

Program codes for implementing the methods of the present disclosure maybe written in one programming language or any combination of moreprogramming languages. These program codes may be provided to aprocessor or controller of a general-purpose computer, a dedicatedcomputer or other programmable data processing apparatus, such that theprogram codes, when executed by the processor or controller, cause thefunctions/operations specified in the flowcharts and/or block diagramsto be implemented. The program codes may be executed entirely on amachine, partially on a machine, partially on a machine and partially ona remote machine as a stand-alone software package or entirely on aremote machine or server.

In the context of the present disclosure, a machine-readable medium maybe a tangible medium that may contain or store a program for use by orin connection with an instruction execution system, an apparatus or adevice. The machine-readable medium may be a machine-readable signalmedium or a machine-readable storage medium. The machine-readable mediummay include, but is not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus or device,or any suitable combination of the above. More specific examples of themachine-readable storage medium may include an electrical connectionbased on one or more wires, a portable computer disk, a hard disk, arandom access memory (RAM), a read only memory (ROM), an erasableprogrammable read only memory (EPROM or a flash memory), an opticalfiber, a compact disk read only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theabove.

In order to provide interaction with the user, the systems andtechnologies described here may be implemented on a computer including adisplay device (for example, a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor) for displaying information to the user, and akeyboard and a pointing device (for example, a mouse or a trackball)through which the user may provide the input to the computer. Othertypes of devices may also be used to provide interaction with the user.For example, a feedback provided to the user may be any form of sensoryfeedback (for example, visual feedback, auditory feedback, or tactilefeedback), and the input from the user may be received in any form(including acoustic input, voice input or tactile input).

The systems and technologies described herein may be implemented in acomputing system including back-end components (for example, a dataserver), or a computing system including middleware components (forexample, an application server), or a computing system includingfront-end components (for example, a user computer having a graphicaluser interface or web browser through which the user may interact withthe implementation of the system and technology described herein), or acomputing system including any combination of such back-end components,middleware components or front-end components. The components of thesystem may be connected to each other by digital data communication (forexample, a communication network) in any form or through any medium.Examples of the communication network include a local area network(LAN), a wide area network (WAN), and the Internet.

The computer system may include a client and a server. The client andthe server are generally far away from each other and usually interactthrough a communication network. The relationship between the client andthe server is generated through computer programs running on thecorresponding computers and having a client-server relationship witheach other. The server may be a cloud server, a server of a distributedsystem, or a server combined with a block-chain.

It should be understood that steps of the processes illustrated abovemay be reordered, added or deleted in various manners. For example, thesteps described in the present disclosure may be performed in parallel,sequentially, or in a different order, as long as a desired result ofthe technical solution of the present disclosure may be achieved. Thisis not limited in the present disclosure.

The above-mentioned specific embodiments do not constitute a limitationon the scope of protection of the present disclosure. Those skilled inthe art should understand that various modifications, combinations,sub-combinations and substitutions may be made according to designrequirements and other factors. Any modifications, equivalentreplacements and improvements made within the spirit and principles ofthe present disclosure shall be contained in the scope of protection ofthe present disclosure.

What is claimed is:
 1. A method of recommending data, the methodcomprising: acquiring operation data of an operation object, wherein theoperation data is associated with first content data and first targetobject data; determining an operation object feature, a content featureand a target object feature based on the operation data; determining afusion feature based on the operation object feature and the contentfeature; and recommending second content data and second target objectdata in an associated manner based on the fusion feature and the targetobject feature.
 2. The method according to claim 1, further comprising:acquiring at least one content label and at least one target objectlabel; wherein the determining an operation object feature, a contentfeature and a target object feature based on the operation datacomprises: determining, based on the operation data, a content labelassociated with the operation object and a target object labelassociated with the operation object from the at least one content labeland the at least one target object label; determining an operationobject label based on the content label associated with the operationobject and the target object label associated with the operation object;determining association graph data based on the operation object label,the content label associated with the operation object, and the targetobject label associated with the operation object; and inputting theassociation graph data into a first deep learning model to obtain theoperation object feature, the content feature and the target objectfeature.
 3. The method according to claim 2, further comprising: beforeacquiring the operation data of the operation object, determining acontent label and a target object label associated with each other,based on a first similarity between the at least one content label andthe at least one target object label, wherein the first similaritybetween the content label and the target object label associated witheach other meets a first similarity condition; and recommending thirdcontent data and third target object data in an associated manner basedon the content label and the target object label associated with eachother, wherein the first content data is at least part of the thirdcontent data, and the first target object data is at least part of thethird target object data.
 4. The method according to claim 2, whereinthe determining, based on the operation data, a content label associatedwith the operation object and a target object label associated with theoperation object from the at least one content label and the at leastone target object label comprises: determining the first content dataand the first target object data corresponding to the operation datafrom the third content data and the third target object data recommendedin the associated manner, wherein the first content data and the firsttarget object data are associated with each other; and determining,based on the first content data and the first target object dataassociated with each other corresponding to the operation data, thecontent label associated with the operation object and the target objectlabel associated with the operation object.
 5. The method according toclaim 1, wherein the recommending second content data and second targetobject data in an associated manner based on the fusion feature and thetarget object feature comprises: determining a second similarity betweenthe fusion feature and the target object feature; and recommending, fora content feature associated with the fusion feature, the second contentdata corresponding to the content feature and the second target objectdata in the associated manner, in response to the second similaritymeeting a second similarity condition.
 6. The method according to claim5, wherein the recommending the second content data corresponding to thecontent feature and the second target object data in the associatedmanner comprises: determining a candidate operation object correspondingto an operation object feature associated with the fusion feature; andrecommending the second content data corresponding to the contentfeature and the second target object data to the candidate operationobject in the associated manner.
 7. The method according to claim 2,wherein the acquiring at least one content label and at least one targetobject label comprises: performing data processing on at least onehistorical content data by using at least one selected from a firstnatural language processing model or a second deep learning model, so asto obtain at least one content label corresponding to each historicalcontent data; and performing data processing on at least one historicaltarget object data by using at least one selected from a second naturallanguage processing model and a third deep learning model, so as toobtain at least one target object label corresponding to each historicaltarget object data.
 8. An electronic device, comprising: at least oneprocessor; and a memory communicatively connected to the at least oneprocessor, wherein the memory stores instructions executable by the atleast one processor, and the instructions, when executed by the at leastone processor, are configured to cause the at least one processor to atleast: acquire operation data of an operation object, wherein theoperation data is associated with first content data and first targetobject data; determine an operation object feature, a content featureand a target object feature based on the operation data; determine afusion feature based on the operation object feature and the contentfeature; and recommend second content data and second target object datain an associated manner based on the fusion feature and the targetobject feature.
 9. The electronic device according to claim 8, whereinthe instructions are further configured to cause the at least oneprocessor to: acquire at least one content label and at least one targetobject label; determine, based on the operation data, a content labelassociated with the operation object and a target object labelassociated with the operation object from the at least one content labeland the at least one target object label; determine an operation objectlabel based on the content label associated with the operation objectand the target object label associated with the operation object;determine association graph data based on the operation object label,the content label associated with the operation object, and the targetobject label associated with the operation object; and input theassociation graph data into a first deep learning model to obtain theoperation object feature, the content feature and the target objectfeature.
 10. The electronic device according to claim 9, wherein theinstructions are further configured to cause the at least one processorto: before acquisition of the operation data of the operation object,determine a content label and a target object label associated with eachother, based on a first similarity between the at least one contentlabel and the at least one target object label, wherein the firstsimilarity between the content label and the target object labelassociated with each other meets a first similarity condition; andrecommend third content data and third target object data in anassociated manner based on the content label and the target object labelassociated with each other, wherein the first content data is at leastpart of the third content data, and the first target object data is atleast part of the third target object data.
 11. The electronic deviceaccording to claim 9, wherein the instructions are further configured tocause the at least one processor to: determine the first content dataand the first target object data corresponding to the operation datafrom the third content data and the third target object data recommendedin the associated manner, wherein the first content data and the firsttarget object data are associated with each other; and determine, basedon the first content data and the first target object data associatedwith each other corresponding to the operation data, the content labelassociated with the operation object and the target object labelassociated with the operation object.
 12. The electronic deviceaccording to claim 8, wherein the instructions are further configured tocause the at least one processor to: determine a second similaritybetween the fusion feature and the target object feature; and recommend,for a content feature associated with the fusion feature, the secondcontent data corresponding to the content feature and the second targetobject data in the associated manner, in response to the secondsimilarity meeting a second similarity condition.
 13. The electronicdevice according to claim 12, wherein the instructions are furtherconfigured to cause the at least one processor to: determine a candidateoperation object corresponding to an operation object feature associatedwith the fusion feature; and recommend the second content datacorresponding to the content feature and the second target object datato the candidate operation object in the associated manner.
 14. Theelectronic device according to claim 9, wherein the instructions arefurther configured to cause the at least one processor to: perform dataprocessing on at least one historical content data by using at least oneselected from a first natural language processing model or a second deeplearning model, so as to obtain at least one content label correspondingto each historical content data; and perform data processing on at leastone historical target object data by using at least one selected from asecond natural language processing model and a third deep learningmodel, so as to obtain at least one target object label corresponding toeach historical target object data.
 15. A non-transitorycomputer-readable storage medium having computer instructions therein,the computer instructions configured to cause a computer system to atleast: acquire operation data of an operation object, wherein theoperation data is associated with first content data and first targetobject data; determine an operation object feature, a content featureand a target object feature based on the operation data; determine afusion feature based on the operation object feature and the contentfeature; and recommend second content data and second target object datain an associated manner based on the fusion feature and the targetobject feature.
 16. The non-transitory computer-readable storage mediumaccording to claim 15, wherein the computer instructions are furtherconfigured to cause the computer system to: acquire at least one contentlabel and at least one target object label; determine, based on theoperation data, a content label associated with the operation object anda target object label associated with the operation object from the atleast one content label and the at least one target object label;determine an operation object label based on the content labelassociated with the operation object and the target object labelassociated with the operation object; determine association graph databased on the operation object label, the content label associated withthe operation object, and the target object label associated with theoperation object; and input the association graph data into a first deeplearning model to obtain the operation object feature, the contentfeature and the target object feature.
 17. The non-transitorycomputer-readable storage medium according to claim 16, wherein thecomputer instructions are further configured to cause the computersystem to: before acquisition of the operation data of the operationobject, determine a content label and a target object label associatedwith each other, based on a first similarity between the at least onecontent label and the at least one target object label, wherein thefirst similarity between the content label and the target object labelassociated with each other meets a first similarity condition; andrecommend third content data and third target object data in anassociated manner based on the content label and the target object labelassociated with each other, wherein the first content data is at leastpart of the third content data, and the first target object data is atleast part of the third target object data.
 18. The non-transitorycomputer-readable storage medium according to claim 16, wherein thecomputer instructions are further configured to cause the computersystem to: determine the first content data and the first target objectdata corresponding to the operation data from the third content data andthe third target object data recommended in the associated manner,wherein the first content data and the first target object data areassociated with each other; and determine, based on the first contentdata and the first target object data associated with each othercorresponding to the operation data, the content label associated withthe operation object and the target object label associated with theoperation object.
 19. The non-transitory computer-readable storagemedium according to claim 15, wherein the computer instructions arefurther configured to cause the computer system to: determine a secondsimilarity between the fusion feature and the target object feature; andrecommend, for a content feature associated with the fusion feature, thesecond content data corresponding to the content feature and the secondtarget object data in the associated manner, in response to the secondsimilarity meeting a second similarity condition.
 20. The non-transitorycomputer-readable storage medium according to claim 19, wherein thecomputer instructions are further configured to cause the computersystem to: determine a candidate operation object corresponding to anoperation object feature associated with the fusion feature; andrecommend the second content data corresponding to the content featureand the second target object data to the candidate operation object inthe associated manner.